GLM 4.7 Feels Like a Real Step Forward for Coding and UI Work
There is always noise when a new AI model drops. Numbers get thrown around, charts get shared, and everyone rushes to compare benchmarks. GLM 4.7 arrived the same way, but once the surface-level talk fades, something more practical starts to show. This release is less about bragging rights and more about how the model actually behaves when asked to build real things.
GLM 4.7 came out a few days ago, and the official research page focuses heavily on coding improvements. The attention is clearly on core development work, multilingual agent workflows, and terminal-based tasks. Compared to GLM 4.6, there are visible gains, including a 5.8 percent jump on SWBench. But the more interesting part is not the number itself. It is what changed under the hood.
GLM 4.7 and why thinking before acting actually matters
One of the most important upgrades in GLM 4.7 is its ability to think before acting. This sounds simple, but in practice it changes how the model handles critical tasks inside agent frameworks. Instead of jumping straight into output, the model plans first. It breaks down what needs to be done, checks dependencies, and then starts executing.
For anyone who has worked with AI agents, this feels familiar. When models rush, things fall apart fast. Files get misplaced, logic gets messy, and half-built features appear. GLM 4.7 feels calmer. Tasks are handled in a more ordered way, which makes complex workflows easier to manage. The result is fewer surprises and less cleanup afterward.
GLM 4.7 coding gains that show up in real work
GLM 4.7 is positioned as a coding-focused upgrade, and that claim mostly holds up. Terminal-based tasks are smoother, multilingual workflows feel more stable, and the model is better at following instructions across multiple steps. Compared to GLM 4.6, the improvement is noticeable when working on longer projects instead of small code snippets.
It stays competitive with models like Claude and GPT in real-world coding tasks. That does not mean it beats them everywhere, but it holds its ground. For developers who care about consistent output rather than flashy demos, this balance matters more than raw scores.
GLM 4.7 vibe coding and why UI quality changed
The research page highlights something called vibe coding. At first glance, it sounds like marketing language. But when looking at the output, the idea makes sense. GLM 4.7 focuses more on visual balance, layout accuracy, and cleaner design choices.
Web pages generated by GLM 4.7 look more modern. Dark mode designs have higher contrast, animations feel smoother, and spacing is handled with more care. Compared to GLM 4.6, the difference is easy to spot. The older version often produced flat layouts that worked but felt unfinished. The newer one feels more polished, like someone actually cared about how it looks.
GLM 4.7 front end showcases tell a clear story
Some front-end examples help explain this shift. A simple website comparison shows GLM 4.7 using a high-contrast dark theme with smoother transitions. The design feels intentional rather than accidental.
In another example, a garden scene looks more refined. Colors blend better, lighting feels softer, and UI elements are cleaner. These are small changes, but together they add up.
The biggest jump shows up in a 3D Rubik’s Cube demo. In GLM 4.6, shuffling the cube only changed colors. There was no real movement. In GLM 4.7, the cube rotates smoothly while shuffling. The solve option works properly, and manual controls respond as expected. This is the kind of detail that separates a demo from something usable.
GLM 4.7 slide generation finally breaks the pattern
Slide generation is another area where GLM 4.7 pulls ahead. In GLM 4.6, most slides followed the same formula. Gradient background, image on the left, text cards on the right. It worked, but it got boring fast.
GLM 4.7 produces slides with better spacing, improved typography, and stronger visual balance. Layouts feel more varied, and content does not look squeezed into a template. It still is not perfect, but it feels closer to something a designer might approve after a quick review.
GLM 4.7 as a free platform that does a lot
All of this runs on a platform that hosts the GLM models. It allows creation of AI-generated slides, full-stack apps, UI designs, code, and even deep research workflows. The surprising part is that it is completely free to use right now.
There is also an API available, which can be integrated into other AI coding tools. Pricing starts around three dollars, making it one of the more affordable options in this space. That low cost is a big reason why GLM stays interesting, even when other models may have stronger outputs in certain areas.
GLM 4.7 building a real app from start to finish
A practical example shows how GLM 4.7 handles a full project. The task is simple on paper. Build a CSV to SQL query generator. Upload a CSV file and convert it into MySQL, PostgreSQL, or SQLite queries. Generate create table and insert statements inside a dark-themed IDE-style UI. Add a landing page with animations.
Once the prompt is submitted, GLM 4.7 starts by planning. It outlines the project structure and creates a task-based to-do list. The entire app is broken into seven clear steps. This makes the process easier to follow and less chaotic.
The app uses Next.js 15 with the app router, TypeScript, Tailwind CSS, and Shad CN UI. Framer Motion handles animations. Prisma is included with an in-built SQLite database. Everything is visible, editable, and downloadable.
One notable change from GLM 4.6 is the package manager. GLM 4.7 uses Bun instead of PNPM. Bun is much faster, so dependency installation finishes quickly. That alone saves time during setup.
GLM 4.7 from build to test without friction
Once development finishes, the app runs ESLint to test code quality. All tasks are marked complete. A summary follows, explaining what was built in each task and how the features fit together.
The UI looks clean. Animations are smooth. The theme feels consistent. Compared to GLM 4.6, design quality is slightly better. Compared to Claude or Gemini, it still sits in the middle. But the cost advantage keeps it attractive.
The app allows CSV uploads, table name selection, database choice, and instant SQL generation. The output can be copied or downloaded as an SQL file. Everything works as expected.
GLM 4.7 authentication and one click deployment
Adding Supabase email authentication is straightforward. Paste the keys, ask GLM to integrate it, and a new task appears. Packages are installed, code updates happen, and within minutes the integration is done. Creating a test account confirms that the connection works.
Deployment is simple. Choose a project name, hit publish, wait a minute or two, and the site goes live on a GLM subdomain. Custom domains are not supported yet, but the full source code can be downloaded and deployed elsewhere.
GLM 4.7 wraps up as a practical upgrade
GLM 4.7 is not trying to win every benchmark race. It is focused on practical changes that show up when building real apps. Better planning, improved UI generation, smoother animations, and a lower price point make it a solid option.
It may not always beat Claude or GPT in design polish, but it delivers reliable results at a fraction of the cost. For developers who care about real-world workflows, GLM 4.7 feels like a clear step forward rather than just another version number. So the real question is simple. Does it fit the way you like to build?
